ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data
ACORN introduces a performant and predicate-agnostic approach for hybrid search, outperforming existing methods with state-of-the-art performance.
Fairness and Diversity in Recommender Systems: A Comprehensive Survey
Fairness and diversity are crucial in recommender systems, with a strong connection between the two domains.
TweetInfo: An Interactive System to Mitigate Harmful Content on Social Media
SNSs에서 유해 콘텐츠 소비를 줄이기 위한 TweetInfo 시스템 소개
GOLF: Goal-Oriented Long-term Life Tasks Supported by Human-AI Collaboration
LLMs can enhance human decision-making and task management through the GOLF framework, focusing on long-term life tasks.
ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data
ACORN introduces a performant and predicate-agnostic approach for hybrid search, outperforming existing methods with state-of-the-art performance.
E-Commerce Ranking System Robustness Analysis
Eコマースランキングシステムの堅牢性に関する研究と提案
TweetInfo: An Interactive System to Mitigate Online Harm
SNSの有害なコンテンツを軽減するためのTweetInfoシステムの提案とデモンストレーション。
Post-Training Attribute Unlearning in Recommender Systems: A Detailed Analysis
The authors focus on Post-Training Attribute Unlearning (PoT-AU) in recommender systems, proposing a two-component loss function to address the challenge. They aim to make target attributes indistinguishable while maintaining recommendation performance.
Enhancing News Recommendation with Generative Paradigm
Incorporating Large Language Models, the generative news recommendation paradigm aims to enhance accuracy and generate personalized narratives.
Fairness and Diversity in Recommender Systems: A Comprehensive Survey
The authors explore the interconnectedness of fairness and diversity in recommender systems, highlighting the importance of considering both aspects for user satisfaction and system performance.
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